There is known “supervised learning” as a technique of machine learning (for example, refer to Patent Literature 1). In supervised learning, a case data set containing combinations of input data (observed data) and output data (implication, attribute, or result of observed data) is regarded as “advice from a supervisor”, and a machine (computer) learns based on the case data set. The phrase “learning” in this context means creating a function model for predicting or estimating output for input data whose output is unknown.
Next, a specific description is given taking facial image recognition as an example. In this facial image recognition, a description is given of a case in which sex (one of human attributes) is estimated based on a facial image.
At the time of learning, a computer constructs a function model based on a case data set containing facial images of females and males. At the time of evaluation, when a facial image (for example, female facial image) whose sex is unknown is supplied, the computer produces “female” as its sex based on the input data and the function model.
As a method of calculating a magnitude of a correlation between an explanatory variable representing a feature value of an object and an objective variable representing an attribute or a result, there are known, for example, a method of calculating a correlation value in a sub-space (one-dimension) of canonical correlation analysis (CCA), maximum likelihood mutual information (MLMI), which is a method of calculating mutual information (MI) (for example, see Non Patent Literature 1), or least-squares mutual information (LSMI), which is a method of calculating squared-loss mutual information (SMI) (for example, see Non Patent Literature 2).